TheBloke
/

guanaco-65B-GPTQ

@@ -1,7 +1,16 @@
 ---
 inference: false
 license: other
 model_type: llama
 ---
 <!-- header start -->
@@ -21,146 +30,196 @@ model_type: llama
 <hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
 <!-- header end -->
-# Tim Dettmers' Guanaco 65B GPTQ
-These files are GPTQ model files for [Tim Dettmers' Guanaco 65B](https://huggingface.co/timdettmers/guanaco-65b).
-Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
-These models were quantised using hardware kindly provided by [Latitude.sh](https://www.latitude.sh/accelerate).
 ## Repositories available
 * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/guanaco-65B-GPTQ)
-* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/guanaco-65B-GGML)
-* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/guanaco-65B-HF)
 ## Prompt template: Guanaco
 ```
 ### Human: {prompt}
 ### Assistant:
 ```
-## Provided files
 Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
 Each separate quant is in a different branch.  See below for instructions on fetching from different branches.
-| Branch | Bits | Group Size | Act Order (desc_act) | File Size | ExLlama Compatible? | Made With | Description |
-| ------ | ---- | ---------- | -------------------- | --------- | ------------------- | --------- | ----------- |
-| main | 4 | None | True | 35.74 GB | True | GPTQ-for-LLaMa | Most compatible option. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. Lower inference quality than other options. |
-| gptq-4bit-32g-actorder_True | 4 | 32 | True | 38.53 GB | True | AutoGPTQ | 4-bit, with Act Order and group size. 32g gives highest possible inference quality, with maximum VRAM usage. Poor AutoGPTQ CUDA speed. |
-| gptq-4bit-64g-actorder_True | 4 | 64 | True | 36.00 GB | True | AutoGPTQ | 4-bit, with Act Order and group size. 64g uses less VRAM than 32g, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. |
-| gptq-4bit-128g-actorder_True | 4 | 128 | True | 34.73 GB | True | AutoGPTQ | 4-bit, with Act Order and group size. 128g uses even less VRAM, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. |
-| gptq-3bit-128g-actorder_False | 3 | 128 | False | 26.57 GB | False | AutoGPTQ | 3-bit, with group size 128g but no act-order. Slightly higher VRAM requirements than 3-bit None. |
-| gptq-3bit-128g-actorder_True | 3 | 128 | True | 26.57 GB | False | AutoGPTQ | 3-bit, with group size 128g and act-order. Higher quality than 128g-False but poor AutoGPTQ CUDA speed. |
-| gptq-3bit-64g-actorder_True | 3 | 64 | True | 27.78 GB | False | AutoGPTQ | 3-bit, with group size 64g and act-order. Highest quality 3-bit option. Poor AutoGPTQ CUDA speed. |
-| gptq-3bit--1g-actorder_True | 3 | None | True | 25.39 GB | False | AutoGPTQ | 3-bit, with Act Order and no group size. Lowest possible VRAM requirements. May be lower quality than 3-bit 128g. |
 ## How to download from branches
-- In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/guanaco-65B-GPTQ:gptq-4bit-32g-actorder_True`
 - With Git, you can clone a branch with:
 ```
-git clone --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/guanaco-65B-GPTQ`
 ```
 - In Python Transformers code, the branch is the `revision` parameter; see below.
 ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
 Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
-It is strongly recommended to use the text-generation-webui one-click-installers unless you know how to make a manual install.
 1. Click the **Model tab**.
 2. Under **Download custom model or LoRA**, enter `TheBloke/guanaco-65B-GPTQ`.
-  - To download from a specific branch, enter for example `TheBloke/guanaco-65B-GPTQ:gptq-4bit-32g-actorder_True`
   - see Provided Files above for the list of branches for each option.
 3. Click **Download**.
-4. The model will start downloading. Once it's finished it will say "Done"
 5. In the top left, click the refresh icon next to **Model**.
 6. In the **Model** dropdown, choose the model you just downloaded: `guanaco-65B-GPTQ`
 7. The model will automatically load, and is now ready for use!
 8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
-  * Note that you do not need to set GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
 9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started!
 ## How to use this GPTQ model from Python code
-First make sure you have [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) installed:
-`GITHUB_ACTIONS=true pip install auto-gptq`
-Then try the following example code:
 ```python
-from transformers import AutoTokenizer, pipeline, logging
-from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
 model_name_or_path = "TheBloke/guanaco-65B-GPTQ"
-model_basename = "Guanaco-65B-GPTQ-4bit-128g.no-act.order"
-use_triton = False
 tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
-model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
-        model_basename=model_basename
-        use_safetensors=True,
-        trust_remote_code=True,
-        device="cuda:0",
-        use_triton=use_triton,
-        quantize_config=None)
-"""
-To download from a specific branch, use the revision parameter, as in this example:
-model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
-        revision="gptq-4bit-32g-actorder_True",
-        model_basename=model_basename,
-        use_safetensors=True,
-        trust_remote_code=True,
-        device="cuda:0",
-        quantize_config=None)
-"""
 prompt = "Tell me about AI"
 prompt_template=f'''### Human: {prompt}
 ### Assistant:
 '''
 print("\n\n*** Generate:")
 input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
-output = model.generate(inputs=input_ids, temperature=0.7, max_new_tokens=512)
 print(tokenizer.decode(output[0]))
 # Inference can also be done using transformers' pipeline
-# Prevent printing spurious transformers error when using pipeline with AutoGPTQ
-logging.set_verbosity(logging.CRITICAL)
 print("*** Pipeline:")
 pipe = pipeline(
     "text-generation",
     model=model,
     tokenizer=tokenizer,
     max_new_tokens=512,
     temperature=0.7,
     top_p=0.95,
-    repetition_penalty=1.15
 )
 print(pipe(prompt_template)[0]['generated_text'])
 ```
 ## Compatibility
-The files provided will work with AutoGPTQ (CUDA and Triton modes), GPTQ-for-LLaMa (only CUDA has been tested), and Occ4m's GPTQ-for-LLaMa fork.
-ExLlama works with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility.
 <!-- footer start -->
 <!-- 200823 -->
@@ -170,10 +229,12 @@ For further support, and discussions on these models and AI in general, join us
 [TheBloke AI's Discord server](https://discord.gg/theblokeai)
-## Thanks, and how to contribute.
 Thanks to the [chirper.ai](https://chirper.ai) team!
 I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
 If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
@@ -185,7 +246,7 @@ Donaters will get priority support on any and all AI/LLM/model questions and req
 **Special thanks to**: Aemon Algiz.
-**Patreon special mentions**: Sam, theTransient, Jonathan Leane, Steven Wood, webtim, Johann-Peter Hartmann, Geoffrey Montalvo, Gabriel Tamborski, Willem Michiel, John Villwock, Derek Yates, Mesiah Bishop, Eugene Pentland, Pieter, Chadd, Stephen Murray, Daniel P. Andersen, terasurfer, Brandon Frisco, Thomas Belote, Sid, Nathan LeClaire, Magnesian, Alps Aficionado, Stanislav Ovsiannikov, Alex, Joseph William Delisle, Nikolai Manek, Michael Davis, Junyu Yang, K, J, Spencer Kim, Stefan Sabev, Olusegun Samson, transmissions 11, Michael Levine, Cory Kujawski, Rainer Wilmers, zynix, Kalila, Luke @flexchar, Ajan Kanaga, Mandus, vamX, Ai Maven, Mano Prime, Matthew Berman, subjectnull, Vitor Caleffi, Clay Pascal, biorpg, alfie_i, 阿明, Jeffrey Morgan, ya boyyy, Raymond Fosdick, knownsqashed, Olakabola, Leonard Tan, ReadyPlayerEmma, Enrico Ros, Dave, Talal Aujan, Illia Dulskyi, Sean Connelly, senxiiz, Artur Olbinski, Elle, Raven Klaugh, Fen Risland, Deep Realms, Imad Khwaja, Fred von Graf, Will Dee, usrbinkat, SuperWojo, Alexandros Triantafyllidis, Swaroop Kallakuri, Dan Guido, John Detwiler, Pedro Madruga, Iucharbius, Viktor Bowallius, Asp the Wyvern, Edmond Seymore, Trenton Dambrowitz, Space Cruiser, Spiking Neurons AB, Pyrater, LangChain4j, Tony Hughes, Kacper Wikieł, Rishabh Srivastava, David Ziegler, Luke Pendergrass, Andrey, Gabriel Puliatti, Lone Striker, Sebastain Graf, Pierre Kircher, Randy H, NimbleBox.ai, Vadim, danny, Deo Leter
 Thank you to all my generous patrons and donaters!
@@ -196,175 +257,56 @@ And thank you again to a16z for their generous grant.
 # Original model card: Tim Dettmers' Guanaco 65B
-# Guanaco Models Based on LLaMA
-| [Paper](https://arxiv.org/abs/2305.14314) | [Code](https://github.com/artidoro/qlora) | [Demo](https://huggingface.co/spaces/uwnlp/guanaco-playground-tgi) |
-**The Guanaco models are open-source finetuned chatbots obtained through 4-bit QLoRA tuning of LLaMA base models on the OASST1 dataset. They are available in 7B, 13B, 33B, and 65B parameter sizes.**
-⚠️Guanaco is a model purely intended for research purposes and could produce problematic outputs.
-## Why use Guanaco?
-- **Competitive with commercial chatbot systems on the Vicuna and OpenAssistant benchmarks** (ChatGPT and BARD) according to human and GPT-4 raters. We note that the relative performance on tasks not covered in these benchmarks could be very different. In addition, commercial systems evolve over time (we used outputs from the March 2023 version of the models).
-- **Available open-source for research purposes**. Guanaco models allow *cheap* and *local* experimentation with high-quality chatbot systems.
-- **Replicable and efficient training procedure** that can be extended to new use cases. Guanaco training scripts are available in the [QLoRA repo](https://github.com/artidoro/qlora).
-- **Rigorous comparison to 16-bit methods** (both 16-bit full-finetuning and LoRA) in [our paper](https://arxiv.org/abs/2305.14314) demonstrates the effectiveness of 4-bit QLoRA finetuning.
-- **Lightweight** checkpoints which only contain adapter weights.
-## License and Intended Use
-Guanaco adapter weights are available under Apache 2 license. Note the use of the Guanaco adapter weights, requires access to the LLaMA model weighs.
-Guanaco is based on LLaMA and therefore should be used according to the LLaMA license.
-## Usage
-Here is an example of how you would load Guanaco 7B in 4-bits:
-```python
-import torch
-from peft import PeftModel
-from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
-model_name = "huggyllama/llama-7b"
-adapters_name = 'timdettmers/guanaco-7b'
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    load_in_4bit=True,
-    torch_dtype=torch.bfloat16,
-    device_map="auto",
-    max_memory= {i: '24000MB' for i in range(torch.cuda.device_count())},
-    quantization_config=BitsAndBytesConfig(
-        load_in_4bit=True,
-        bnb_4bit_compute_dtype=torch.bfloat16,
-        bnb_4bit_use_double_quant=True,
-        bnb_4bit_quant_type='nf4'
-    ),
-)
-model = PeftModel.from_pretrained(model, adapters_name)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-```
-Inference can then be performed as usual with HF models as follows:
-```python
-prompt = "Introduce yourself"
-formatted_prompt = (
-    f"A chat between a curious human and an artificial intelligence assistant."
-    f"The assistant gives helpful, detailed, and polite answers to the user's questions.\n"
-    f"### Human: {prompt} ### Assistant:"
-)
-inputs = tokenizer(formatted_prompt, return_tensors="pt").to("cuda:0")
-outputs = model.generate(inputs=inputs.input_ids, max_new_tokens=20)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
-```
-Expected output similar to the following:
-```
-A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
-### Human: Introduce yourself ### Assistant: I am an artificial intelligence assistant. I am here to help you with any questions you may have.
-```
-## Current Inference Limitations
-Currently, 4-bit inference is slow. We recommend loading in 16 bits if inference speed is a concern. We are actively working on releasing efficient 4-bit inference kernels.
-Below is how you would load the model in 16 bits:
-```python
-model_name = "huggyllama/llama-7b"
-adapters_name = 'timdettmers/guanaco-7b'
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    torch_dtype=torch.bfloat16,
-    device_map="auto",
-    max_memory= {i: '24000MB' for i in range(torch.cuda.device_count())},
-)
-model = PeftModel.from_pretrained(model, adapters_name)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-```
-## Model Card
-**Architecture**: The Guanaco models are LoRA adapters to be used on top of LLaMA models. They are added to all layers. For all model sizes, we use $r=64$.
-**Base Model**: Guanaco uses LLaMA as base model with sizes 7B, 13B, 33B, 65B. LLaMA is a causal language model pretrained on a large corpus of text. See [LLaMA paper](https://arxiv.org/abs/2302.13971) for more details. Note that Guanaco can inherit biases and limitations of the base model.
-**Finetuning Data**: Guanaco is finetuned on OASST1. The exact dataset is available at [timdettmers/openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco).
-**Languages**: The OASST1 dataset is multilingual (see [the paper](https://arxiv.org/abs/2304.07327) for details) and as such Guanaco responds to user queries in different languages. We note, however, that OASST1 is heavy in high-resource languages. In addition, human evaluation of Guanaco was only performed in English and based on qualitative analysis we observed degradation in performance in other languages.
-Next, we describe Training and Evaluation details.
-### Training
-Guanaco models are the result of 4-bit QLoRA supervised finetuning on the OASST1 dataset.
-All models use NormalFloat4 datatype for the base model and LoRA adapters on all linear layers with BFloat16 as computation datatype. We set LoRA $r=64$, $\alpha=16$. We also use Adam beta2 of 0.999, max grad norm of 0.3 and LoRA dropout of 0.1 for models up to 13B and 0.05 for 33B and 65B models.
-For the finetuning process, we use constant learning rate schedule and paged AdamW optimizer.
-### Training hyperparameters
-Size| Dataset | Batch Size | Learning Rate | Max Steps | Sequence length
----|---|---|---|---|---
-7B | OASST1      | 16 | 2e-4 | 1875 | 512
-13B | OASST1     | 16 | 2e-4 | 1875 | 512
-33B | OASST1     | 16 | 1e-4 | 1875 | 512
-65B | OASST1     | 16 | 1e-4 | 1875 | 512
-### Evaluation
-We test generative language capabilities through both automated and human evaluations. This second set of evaluations relies on queries curated by humans and aims at measuring the quality of model responses. We use the Vicuna and OpenAssistant datasets with 80 and 953 prompts respectively.
-In both human and automated evaluations, for each prompt, raters compare all pairs of responses across the models considered. For human raters we randomize the order of the systems, for GPT-4 we evaluate with both orders.
-Benchmark | Vicuna |  | Vicuna |   | OpenAssistant |   | -
------------|----|-----|--------|---|---------------|---|---
-Prompts    | 80 |     | 80     |   | 953           |   |
-Judge | Human | | GPT-4 | | GPT-4 | |
-Model | Elo | Rank | Elo | Rank | Elo | Rank | **Median Rank**
-GPT-4 | 1176 | 1 | 1348 | 1 | 1294 | 1 | 1
-Guanaco-65B | 1023 | 2 | 1022 | 2 | 1008 | 3 | 2
-Guanaco-33B | 1009 | 4 | 992 | 3 | 1002 | 4 | 4
-ChatGPT-3.5 Turbo | 916 | 7 | 966 | 5 | 1015 | 2 | 5
-Vicuna-13B | 984 | 5 | 974 | 4 | 936 | 5 | 5
-Guanaco-13B | 975 | 6 | 913 | 6 | 885 | 6 | 6
-Guanaco-7B | 1010 | 3 | 879 | 8 | 860 | 7 | 7
-Bard | 909 | 8 | 902 | 7 | - | - | 8
-We also use the MMLU benchmark to measure performance on a range of language understanding tasks. This is a multiple-choice benchmark covering 57 tasks including elementary mathematics, US history, computer science, law, and more. We report 5-shot test accuracy.
- Dataset | 7B | 13B | 33B | 65B
----|---|---|---|---
- LLaMA no tuning | 35.1 | 46.9 | 57.8 | 63.4
- Self-Instruct | 36.4 | 33.3 | 53.0 | 56.7
- Longform | 32.1 | 43.2 | 56.6 | 59.7
- Chip2 | 34.5 | 41.6 | 53.6 | 59.8
- HH-RLHF | 34.9 | 44.6 | 55.8 | 60.1
- Unnatural Instruct | 41.9 | 48.1 | 57.3 | 61.3
- OASST1 (Guanaco) | 36.6 | 46.4 | 57.0 | 62.2
- Alpaca | 38.8 | 47.8 | 57.3 | 62.5
- FLAN v2 | 44.5 | 51.4 | 59.2 | 63.9
-## Risks and Biases
-The model can produce factually incorrect output, and should not be relied on to produce factually accurate information. The model was trained on various public datasets; it is possible that this model could generate lewd, biased, or otherwise offensive outputs.
-However, we note that finetuning on OASST1 seems to reduce biases as measured on the CrowS dataset. We report here the performance of Guanaco-65B compared to other baseline models on the CrowS dataset.
-|                      | LLaMA-65B | GPT-3 | OPT-175B | Guanaco-65B   |
-|----------------------|-----------|-------|----------|---------------|
-| Gender               | 70.6      | 62.6  | 65.7     | **47.5** |
-| Religion             | {79.0}    | 73.3  | 68.6     | **38.7** |
-| Race/Color           | 57.0      | 64.7  | 68.6     | **45.3** |
-| Sexual orientation   | {81.0}    | 76.2  | 78.6     | **59.1** |
-| Age                  | 70.1      | 64.4  | 67.8     | **36.3** |
-| Nationality          | 64.2      | 61.6  | 62.9     | **32.4** |
-| Disability           | 66.7      | 76.7  | 76.7     | **33.9** |
-| Physical appearance  | 77.8      | 74.6  | 76.2     | **43.1** |
-| Socioeconomic status | 71.5      | 73.8  | 76.2     | **55.3** |
-| Average              | 66.6      | 67.2  | 69.5     | **43.5** |
-## Citation
-```bibtex
-@article{dettmers2023qlora,
-  title={QLoRA: Efficient Finetuning of Quantized LLMs},
-  author={Dettmers, Tim and Pagnoni, Artidoro and Holtzman, Ari and Zettlemoyer, Luke},
-  journal={arXiv preprint arXiv:2305.14314},
-  year={2023}
-}
-```

 ---
+base_model: https://huggingface.co/timdettmers/guanaco-65b
 inference: false
 license: other
+model_creator: Tim Dettmers
+model_name: Guanaco 65B
 model_type: llama
+prompt_template: '### Human: {prompt}
+  ### Assistant:
+  '
+quantized_by: TheBloke
 ---
 <!-- header start -->
 <hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
 <!-- header end -->
+# Guanaco 65B - GPTQ
+- Model creator: [Tim Dettmers](https://huggingface.co/timdettmers)
+- Original model: [Guanaco 65B](https://huggingface.co/timdettmers/guanaco-65b)
+<!-- description start -->
+## Description
+This repo contains GPTQ model files for [Tim Dettmers' Guanaco 65B](https://huggingface.co/timdettmers/guanaco-65b).
+Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
+<!-- description end -->
+<!-- repositories-available start -->
 ## Repositories available
+* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/guanaco-65B-AWQ)
 * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/guanaco-65B-GPTQ)
+* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/guanaco-65B-GGUF)
+* [Tim Dettmers's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/guanaco-65B-HF)
+<!-- repositories-available end -->
+<!-- prompt-template start -->
 ## Prompt template: Guanaco
 ```
 ### Human: {prompt}
 ### Assistant:
 ```
+<!-- prompt-template end -->
+<!-- licensing start -->
+## Licensing
+The creator of the source model has listed its license as `other`, and this quantization has therefore used that same license.
+As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
+In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [Tim Dettmers' Guanaco 65B](https://huggingface.co/timdettmers/guanaco-65b).
+<!-- licensing end -->
+<!-- README_GPTQ.md-provided-files start -->
+## Provided files and GPTQ parameters
 Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
 Each separate quant is in a different branch.  See below for instructions on fetching from different branches.
+All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches are made with AutoGPTQ. Files in the `main` branch which were uploaded before August 2023 were made with GPTQ-for-LLaMa.
+<details>
+  <summary>Explanation of GPTQ parameters</summary>
+- Bits: The bit size of the quantised model.
+- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
+- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
+- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
+- GPTQ dataset: The dataset used for quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
+- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used.  Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
+- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit.
+</details>
+| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
+| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
+| [main](https://huggingface.co/TheBloke/guanaco-65B-GPTQ/tree/main) | 4 | 128 | No | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 2048 | 33.49 GB | Yes | 4-bit, without Act Order and group size 128g. |
+| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/guanaco-65B-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 2048 | 38.53 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
+| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/guanaco-65B-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 2048 | 36.00 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |
+| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/guanaco-65B-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 2048 | 34.73 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
+| [gptq-3bit-128g-actorder_False](https://huggingface.co/TheBloke/guanaco-65B-GPTQ/tree/gptq-3bit-128g-actorder_False) | 3 | 128 | No | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 2048 | 26.57 GB | No | 3-bit, with group size 128g but no act-order. Slightly higher VRAM requirements than 3-bit None. |
+| [gptq-3bit-128g-actorder_True](https://huggingface.co/TheBloke/guanaco-65B-GPTQ/tree/gptq-3bit-128g-actorder_True) | 3 | 128 | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 2048 | 26.57 GB | No | 3-bit, with group size 128g and act-order. Higher quality than 128g-False. |
+| [gptq-3bit-64g-actorder_True](https://huggingface.co/TheBloke/guanaco-65B-GPTQ/tree/gptq-3bit-64g-actorder_True) | 3 | 64 | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 2048 | 27.78 GB | No | 3-bit, with group size 64g and act-order. |
+| [gptq-3bit--1g-actorder_True](https://huggingface.co/TheBloke/guanaco-65B-GPTQ/tree/gptq-3bit--1g-actorder_True) | 3 | None | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 2048 | 25.39 GB | No | 3-bit, with Act Order and no group size. Lowest possible VRAM requirements. May be lower quality than 3-bit 128g. |
+<!-- README_GPTQ.md-provided-files end -->
+<!-- README_GPTQ.md-download-from-branches start -->
 ## How to download from branches
+- In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/guanaco-65B-GPTQ:main`
 - With Git, you can clone a branch with:
 ```
+git clone --single-branch --branch main https://huggingface.co/TheBloke/guanaco-65B-GPTQ
 ```
 - In Python Transformers code, the branch is the `revision` parameter; see below.
+<!-- README_GPTQ.md-download-from-branches end -->
+<!-- README_GPTQ.md-text-generation-webui start -->
 ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
 Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
+It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
 1. Click the **Model tab**.
 2. Under **Download custom model or LoRA**, enter `TheBloke/guanaco-65B-GPTQ`.
+  - To download from a specific branch, enter for example `TheBloke/guanaco-65B-GPTQ:main`
   - see Provided Files above for the list of branches for each option.
 3. Click **Download**.
+4. The model will start downloading. Once it's finished it will say "Done".
 5. In the top left, click the refresh icon next to **Model**.
 6. In the **Model** dropdown, choose the model you just downloaded: `guanaco-65B-GPTQ`
 7. The model will automatically load, and is now ready for use!
 8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
+  * Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
 9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started!
+<!-- README_GPTQ.md-text-generation-webui end -->
+<!-- README_GPTQ.md-use-from-python start -->
 ## How to use this GPTQ model from Python code
+### Install the necessary packages
+Requires: Transformers 4.32.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
+```shell
+pip3 install transformers>=4.32.0 optimum>=1.12.0
+pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/  # Use cu117 if on CUDA 11.7
+```
+If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:
+```shell
+pip3 uninstall -y auto-gptq
+git clone https://github.com/PanQiWei/AutoGPTQ
+cd AutoGPTQ
+pip3 install .
+```
+### For CodeLlama models only: you must use Transformers 4.33.0 or later.
+If 4.33.0 is not yet released when you read this, you will need to install Transformers from source:
+```shell
+pip3 uninstall -y transformers
+pip3 install git+https://github.com/huggingface/transformers.git
+```
+### You can then use the following code
 ```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 model_name_or_path = "TheBloke/guanaco-65B-GPTQ"
+# To use a different branch, change revision
+# For example: revision="main"
+model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
+                                             device_map="auto",
+                                             trust_remote_code=True,
+                                             revision="main")
 tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
 prompt = "Tell me about AI"
 prompt_template=f'''### Human: {prompt}
 ### Assistant:
 '''
 print("\n\n*** Generate:")
 input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
+output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
 print(tokenizer.decode(output[0]))
 # Inference can also be done using transformers' pipeline
 print("*** Pipeline:")
 pipe = pipeline(
     "text-generation",
     model=model,
     tokenizer=tokenizer,
     max_new_tokens=512,
+    do_sample=True,
     temperature=0.7,
     top_p=0.95,
+    top_k=40,
+    repetition_penalty=1.1
 )
 print(pipe(prompt_template)[0]['generated_text'])
 ```
+<!-- README_GPTQ.md-use-from-python end -->
+<!-- README_GPTQ.md-compatibility start -->
 ## Compatibility
+The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with [Occ4m's GPTQ-for-LLaMa fork](https://github.com/0cc4m/KoboldAI).
+[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility.
+[Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is compatible with all GPTQ models.
+<!-- README_GPTQ.md-compatibility end -->
 <!-- footer start -->
 <!-- 200823 -->
 [TheBloke AI's Discord server](https://discord.gg/theblokeai)
+## Thanks, and how to contribute
 Thanks to the [chirper.ai](https://chirper.ai) team!
+Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
 I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
 If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
 **Special thanks to**: Aemon Algiz.
+**Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov
 Thank you to all my generous patrons and donaters!
 # Original model card: Tim Dettmers' Guanaco 65B
+<!-- header start -->
+<div style="width: 100%;">
+    <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
+</div>
+<div style="display: flex; justify-content: space-between; width: 100%;">
+    <div style="display: flex; flex-direction: column; align-items: flex-start;">
+        <p><a href="https://discord.gg/Jq4vkcDakD">Chat & support: my new Discord server</a></p>
+    </div>
+    <div style="display: flex; flex-direction: column; align-items: flex-end;">
+        <p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
+    </div>
+</div>
+<!-- header end -->
+# Tim Dettmers' Guanaco 65B fp16 HF
+These files are fp16 HF model files for [Tim Dettmers' Guanaco 65B](https://huggingface.co/timdettmers/guanaco-65b).
+It is the result of merging the LoRA then saving in HF fp16 format.
+## Other repositories available
+* [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/guanaco-65B-GPTQ)
+* [4-bit, 5-bit and 8-bit GGML models for CPU(+GPU) inference](https://huggingface.co/TheBloke/guanaco-65B-GGML)
+* [Merged, unquantised fp16 model in HF format](https://huggingface.co/TheBloke/guanaco-65B-HF)
+<!-- footer start -->
+## Discord
+For further support, and discussions on these models and AI in general, join us at:
+[TheBloke AI's Discord server](https://discord.gg/Jq4vkcDakD)
+## Thanks, and how to contribute.
+Thanks to the [chirper.ai](https://chirper.ai) team!
+I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
+If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
+Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
+* Patreon: https://patreon.com/TheBlokeAI
+* Ko-Fi: https://ko-fi.com/TheBlokeAI
+**Patreon special mentions**: Aemon Algiz, Dmitriy Samsonov, Nathan LeClaire, Trenton Dambrowitz, Mano Prime, David Flickinger, vamX, Nikolai Manek, senxiiz, Khalefa Al-Ahmad, Illia Dulskyi, Jonathan Leane, Talal Aujan, V. Lukas, Joseph William Delisle, Pyrater, Oscar Rangel, Lone Striker, Luke Pendergrass, Eugene Pentland, Sebastain Graf, Johann-Peter Hartman.
+Thank you to all my generous patrons and donaters!
+<!-- footer end -->
+# Original model card
+Not provided by original model creator.